Code Author: Mark E. Pepin MD, PhD

Corresponding Author: Maarten Van Den Hoogenhoff, PhD

Contact:

Institution: Heidelberg University Hospital

Location: 669 Neuenheimer Feld, Institute for Experimental Cardiology, 69120 Heidelberg, Germany

Preliminary Setup

Packages

if (!require("pacman")) install.packages("pacman")
pacman::p_load(dplyr, Hmisc, openxlsx, corrplot, RColorBrewer, kableExtra, ggplot2, gridExtra, ggpubr, ggsignif, DESeq2, data.table, GenomicFeatures, biomaRt, Haplin, pheatmap, calibrate, ggrepel, tidyr, gtools, rlist, limma, wesanderson,plotly)

Parameters

Define the parameters used, along with the conditions required for the current analysis. This segment must be modified for each analysis performed.

start_t<-Sys.time()
##Set the experimental conditions [MUST DO THIS MANUALLY]
INVEST=c("Maarten")
GENOTYPE=c("WT","RBM20.KO") 
GROUP=c("F_WT", "F_RBM20.KO")
SEX=c("F")
# OUTLIER=c("676")
STATISTIC = 0.05 #P statistic threshold used in this combination.
# Set the "variable" that determines differential expression
VARIABLE = GROUP
COMPARISON= paste0(SEX[1],"_", GENOTYPE[2], ".v.", GENOTYPE[1])
colData_all <- openxlsx::read.xlsx("../1_Input/colData.xlsx")
colData_all$Group<-factor(colData_all$Group, levels = c("M_WT", "F_WT", "M_RBM20.KO", "F_RBM20.KO"))
#Colors
# Candidate Gene Selection (RNA-sequencing) EDIT THIS LIST BASED ON INTERESTS.
GENES=c("Rbm20", "Camk2d", "Camk2g", "Ttn")
my_comparisons_all <- list(c("F_WT", "M_WT"), c("M_WT", "M_RBM20.KO"), c("F_WT", "F_RBM20.KO"), c("F_RBM20.KO", "M_RBM20.KO"))
my_comparisons_female <- list(c("F_WT", "F_RBM20.KO"))
my_comparisons_male <- list(c("M_WT", "M_RBM20.KO"))
## Create color based on Genotype
ann_colors = list(Group = c(F_WT="grey", M_WT = "darkcyan", F_RBM20.KO ="coral2", M_RBM20.KO = "dodgerblue2"))
ann_colorTable<-as.data.frame(ann_colors)
ann_colGroup<-subset(ann_colorTable, rownames(ann_colorTable) %in% GROUP) #Creates colors just for the specific comparison (by GROUP)
ann_colListGroup<-list(ann_colGroup)
ann_colGroupVec<-ann_colGroup$Group
ann_colorInvest<-subset(ann_colorTable, rownames(ann_colorTable) %in% colData_all$Group) #Creates colors just for the specific comparison (by INVEST)
ann_colListInvest<-list(ann_colorInvest$Group)
ann_colorInvestVec<-ann_colorInvest$Group
names(ann_colorInvestVec)<-as.factor(rownames(ann_colorInvest))
INVEST_colors<-list(ann_colorInvestVec=ann_colorInvestVec)
names(ann_colGroupVec)<-as.factor(rownames(ann_colGroup))
GROUP_colors<-list(ann_colGroupVec=ann_colGroupVec)

# Create Output Folder Structure
ifelse(!dir.exists(file.path(paste0("../2_Output/"))), dir.create(file.path(paste0("../2_Output/"))), FALSE)
## [1] FALSE

RNA-Sequencing Analysis

RNA-Sequencing Alignment using STAR (v2.7.10a)

RNA was isolated from the left ventricle endocardial tissue using the RNeasy Lipid Mini-Kit according to the manufacturer’s instructions (Qiagen, Valencia, CA). High-throughput RNA sequencing was performed at BGI (Hong Kong, CN). Once sample read quality was checked (FastQC -> multiQC analysis), the paired-end fastq files were then aligned to the reference genome, which was created using Gencode mouse sequence (GRCm38.primary_assembly.genome.fa) and annotation (gencode.vM25.primary_assembly.annotation.gtf). STAR aligner (2.7.10a) is the current gold-standard for this, which we used for the current analysis. Before aligning each fastq file to the genome, an annotated reference genome must first be assembled. This was performed as follows (this was performed in Cheaha as `bash GenomeReference.sh’:

rclone Philip_RNASeq/ gdrive:fasrc/Subfolder –progress

STAR=../../Tools/STAR-2.7.10a/bin/Linux_x86_64/STAR $STAR \ --runThreadN 8 \ --runMode genomeGenerate \ --genomeDir ./ \ --genomeFastaFiles [path-to-"GRCm38.primary_assembly.genome.fa"] \ --sjdbOverhang 100 \ --sjdbGTFfile ${GTF} \

Alignment of short reads to this annotated genome could then proceed, using the following SLURM batch script which was submitted to the UAB Cheaha compute cluster (See Attached Shell script). This shell script contains the following STAR alignment run settings:

$STAR_RUN \ --genomeDir $GENOME_DIR \ --readFilesCommand zcat \ --readFilesIn $INPUT_DIR/fastq/${VAR}_1.fastq.gz $INPUT_DIR/fastq/${VAR}_2.fastq.gz \ --sjdbGTFfile $GENOME_DIR/gencode.vM25.chr_patch_hapl_scaff.annotation.gtf \ --quantMode GeneCounts \ --runThreadN 12 \ --outSAMtype BAM SortedByCoordinate \ --outFileNamePrefix ${RESULTS_DIR}/Alignment/${VAR}_

Read Count Compiling

Before the DESeq2-based differential expression can be computed, the counts generated by STAR need to be compiled, since the .tab file contains count information based on forward, reverse, and combined reads. Therefore, we will take the fourth column in each table and merge them.

Count.files <- list.files(path = "../1_Input/Counts/", pattern = "*_ReadsPerGene.out.tab", full.names = TRUE, all.files = TRUE)
Counts <- lapply(Count.files, read.table, skip = 4) #skip the first 4 rows, since these are summary data.
#Create a data.frame containing the raw counts
countData.raw <- as.data.frame(sapply(Counts, function(x) x[,2])) #selects only the 4th column raw counts.
#Generate Column names and Row names for the counts (remove the extra nonsense from the path names)
colnames <- gsub( "_ReadsPerGene[.]out[.]tab", "", Count.files)
colnames <- gsub( ".*-", "", colnames) # removes everything before the hyphen
colnames(countData.raw) <- colnames
row.names(countData.raw) <- Counts[[1]][,1]

Data Pre-Processing

After alignment of the fastq files to the annotated genome assembly (mm10), the first step in the analysis is to consolidate the raw data from the provided files into data matrix that can be used to generate a normalized count matrix and differential expression dataset.

## [1] FALSE

Count Normalization

DESeq2 (version 1.34.0) was used to perform the raw count normalization within R (version 4.1.2) and pairwise differential expression according to genotype.

######### RUN DESeq2
dds<-DESeqDataSetFromMatrix(countData=countData, colData = colData, design= ~Group)
dds
## class: DESeqDataSet 
## dim: 55414 20 
## metadata(1): version
## assays(1): counts
## rownames(55414): ENSMUSG00000102693.2 ENSMUSG00000064842.3 ...
##   ENSMUSG00000095019.2 ENSMUSG00000095041.8
## rowData names(0):
## colnames(20): 567 568 ... 530 531
## colData names(5): Sample_ID Mouse_ID Sex Genotype Group
#Determine the Dispersion Relationship (determines which distribution to use for the differential analysis) - should take about 2 minutes
dds <- estimateSizeFactors(dds)
dds <- estimateDispersions(dds)
plotDispEsts(dds)

png(file=paste0("../2_Output/", COMPARISON, "/", COMPARISON, "_Dispersion.png"))
plotDispEsts(dds)
dev.off()
## quartz_off_screen 
##                 2

There appears to be a linear negative correlation between the mean and dispersion estimates, so the parametric “Wald” model should be an appropriate fit for differential expression analysis. Furthermore, we get away with the parametric fit-type owing to the apparent negative binomial distribution. NOTE: If it were nonlinear throughout, we would require a ‘local’ nonparametric fit-type.

Differential Expression Analysis

##Pre-Filter to reduce the size of this dataset (according to the DESeq2 document reccomendations)
dds <- dds[ rowSums(counts(dds)) > 1, ]
dds
## class: DESeqDataSet 
## dim: 25462 20 
## metadata(1): version
## assays(2): counts mu
## rownames(25462): ENSMUSG00000051951.6 ENSMUSG00000102331.2 ...
##   ENSMUSG00000095742.2 ENSMUSG00000095041.8
## rowData names(10): baseMean baseVar ... dispOutlier dispMAP
## colnames(20): 567 568 ... 530 531
## colData names(6): Sample_ID Mouse_ID ... Group sizeFactor
################Run DESeq2 differential quantification (Likelihood ratio test (LRT) or Wald-test)
dds<-DESeq(dds, test="Wald", fitType="parametric")
#compile the results tables
resultsNames(dds)
## [1] "Intercept"                "Group_F_RBM20.KO_vs_F_WT"
resdf<-as.data.frame(results(dds,format = "DataFrame"))
resdf$ensembl_gene_id<-as.character(row.names(resdf))

Once the differential Expression analysis was performed, the following were compiled into a results data matrix: Log2FoldChange, P-value, Bonferroni-Adjusted P-Value (Q-value), and normalized counts for each sample.

####Add Annotation to the results file (this will take some time, about 5 minutes...)
gtf.file="../1_Input/Genome/gencode.vM28.primary_assembly.annotation.gtf"
gtf.gr = rtracklayer::import(gtf.file) # creates a GRanges object
gtf.df = as.data.frame(gtf.gr)
genes = unique(gtf.df[ ,c("gene_id","gene_name")])
Test1<-gtf.df %>% filter(type=="gene") %>% dplyr::select(ensembl_gene_id=gene_id, gene_name, seqnames:strand)
# genes = unique(Test1[ ,c("ensembl_gene_id","gene_name")])
results<-merge(resdf, Test1, by="ensembl_gene_id")
Residual<-dplyr::anti_join(resdf, results, by = "ensembl_gene_id")

####Add normalized count data (for heatmap and sota)
normcount<-as.data.frame(counts(dds, normalized=TRUE))
normcount$ensembl_gene_id<-rownames(normcount)
results<-dplyr::left_join(results, normcount, by="ensembl_gene_id")
results<-results[order(results$pvalue),] # order table by pvalue
##Create a Counts table with annotated Gene name
Counts_table<-results[,9:ncol(results)]
Counts_table<-cbind(results$gene_name, Counts_table)
rownames(Counts_table)<-results$ensembl_gene_id
write.xlsx(Counts_table, paste0("../2_Output/", COMPARISON, "/", COMPARISON, "_Normalized.Counts.xlsx"), rowNames = FALSE, overwrite = T)

#Create Rlog transformed data
rld<-rlog(dds)
rld<-assay(rld) #transpose the rlog-normalized count data
rld<-as.data.frame(rld) #convert to a data frame (to merge the colData)
write.xlsx(rld, paste0("../2_Output/", COMPARISON, "/", COMPARISON, "_rlog.counts.xlsx"), rowNames = TRUE, overwrite = T)

#Create filters as tabs
results_p05<-dplyr::filter(results, pvalue<STATISTIC) 
results_q05<-dplyr::filter(results, padj<STATISTIC)
library(openxlsx)
wb_DESeq<-createWorkbook()
#Unfiltered
  addWorksheet(wb_DESeq, "Unfiltered")
  writeData(wb_DESeq, "Unfiltered", results, startCol = 1)
#P-value Significant (0.05)
  addWorksheet(wb_DESeq, "P_0.05")
  writeData(wb_DESeq, "P_0.05", results_p05, startCol = 1)
#Q-value Significant (0.05)
  addWorksheet(wb_DESeq, "Q_0.05")
  writeData(wb_DESeq, "Q_0.05", results_q05, startCol = 1)
saveWorkbook(wb_DESeq, file = paste0("../2_Output/", COMPARISON, "/", COMPARISON,"_DESeq2.xlsx"), overwrite = TRUE)

MDS Plot - Transcriptomics

Unsupervised MDS demonstrates a sex difference, yet a weak separation between genotypes (Supplemental Figure S5).

library(limma)
library(openxlsx)
# Perform MDS on RNA-sequencing data
MDS_data<-read.xlsx(paste0("../1_Input/rlog.counts.xlsx"), rowNames = FALSE)
Index_MDS<-read.xlsx("../1_Input/colData.xlsx")
rownames(Index_MDS)<-Index_MDS$Sample_ID
Index_MDS$Sample_ID<-as.character(Index_MDS$Sample_ID)
options(ggrepel.max.overlaps = Inf)
#Filter normalized counts to remove outliers
vector<-Index_MDS$Sample_ID
MDS_data<-dplyr::select(MDS_data, all_of(vector), -`results$gene_name`)
# MDS in ggplot2
Ntop = 10000
library(magrittr)
library(dplyr)
library(ggpubr)
library(matrixStats)
library("ggrepel")
library(wesanderson)
MDS.set<-as.data.frame(MDS_data)
RowVar<-rowVars(data.matrix(MDS.set)) #calculate variances for each row (vector)
MDS.set<-as.data.frame(cbind(MDS.set, RowVar)) #Add to the MDS.set dataset
MDS_matrix<-MDS.set %>% arrange(desc(RowVar)) %>% top_n(Ntop,RowVar) #Select top N rows by variance
# Compute MDS
mds <- MDS_matrix %>% dplyr::select(-RowVar) %>% t(.) %>%
  dist() %>%          
  cmdscale() %>%
  as_tibble()
colnames(mds) <- c("Dim.1", "Dim.2")
rownames(mds)<-rownames(Index_MDS)
mds$Sample_ID<-rownames(mds)
mds<-dplyr::inner_join(mds, Index_MDS)
#K-means clustering
clust <- kmeans(mds[,1:2], 2)$cluster %>%
  as.factor()
mds <- mds %>%
  mutate(kmeans.2 = clust)
###
library(ggpubr)
library(cowplot) 
# Main plot
pmain <- ggplot(mds, aes(x = Dim.1, y = Dim.2, color = Group))+
  # scale_color_manual(values = ann_colorInvestVec) +
  theme(panel.background = element_rect("white", colour = "black", size=2), 
      panel.grid.major = element_line(colour = "gray50", size=.75), 
      panel.grid.minor = element_line(colour = "gray50", size=0.4),
      legend.position="bottom",
      legend.key=element_blank(),
      axis.text = element_text(size = 12),
      axis.title = element_text(size = 14, face="bold")) +
  geom_hline(yintercept = 0, size = 1) + 
  geom_vline(xintercept=0, size=1) +
  geom_point()+ #Add points for each sample
  # stat_ellipse()+ # create elliptical shapes
  geom_text_repel(data=mds, aes(label=Sample_ID), show.legend  = F) + #label the samples
  labs(x="Principal Component 1", 
       y="Principal Component 2")
# Marginal densities along x axis
xdens <- axis_canvas(pmain, axis = "x")+
  geom_density(data = mds, aes(x = Dim.1, fill = Group),
              alpha = 0.7, size = 0.2)
  # scale_fill_manual(values = ann_colorInvestVec)
# Marginal densities along y axis
ydens <- axis_canvas(pmain, axis = "y", coord_flip = TRUE)+ #must set coord_flip = true if using coord_flip() below
  geom_density(data = mds, aes(x = Dim.2, fill = Group),
                alpha = 0.7, size = 0.2)+
  coord_flip()
  # scale_fill_manual(values = ann_colorInvestVec)
p1 <- insert_xaxis_grob(pmain, xdens, grid::unit(.2, "null"), position = "top")
p2<- insert_yaxis_grob(p1, ydens, grid::unit(.2, "null"), position = "right")
pdf(file=paste0("../2_Output/MDS.Scatterhist.pdf"), height = 5, width = 5, onefile = F)
ggdraw(p2)
dev.off()
## quartz_off_screen 
##                 2
ggdraw(p2)

QQ Plot

Before we examined the gene networks and pathways differentially regulated between male and female mice according to genotype, the first task was to determine whether transgene induction resulted in global changes. An effective way of determining this is the QQ plot, which compares the P-value distribution produced by the pairwise comparison to that of a random normal distribution. Below, it is evident that differential expression according to patient race yields a robustly divergent expression pattern, one that likely reflects population differences in gene activation.

#Create Q-Q plot
test<-results
test<-test[complete.cases(test),]
pQQ(test$pvalue, lim=c(0,10))

png(file=paste0("../2_Output/", COMPARISON,  "/", COMPARISON,"_QQ.Plot.png"))
pQQ(test$pvalue, lim=c(0,10))
dev.off()
## quartz_off_screen 
##                 2

Principal Components Analysis

Once we established that the populations under consideration truly display disparate expression patterns, we determine whether unbiased global gene expression patterns recapitulate the described phenotypes within each phenotype/genotype. To accomplish this, an unsupervised Principal Components Analysis (PCA) was initially used with normalized counts.

PCA Features

Before running the principal components analysis, it was necessary to first determine the number of PC’s required to account for 80% of the variance, a machine-learning algorithmm benchmark that provides sufficient confidence in the analysis.

#Plot Features of the PCA
library(dplyr)
library(plotly)
##Import the data to be used for PCA
PCA_data<-read.xlsx("../1_Input/rlog.counts.xlsx", rowNames = TRUE)
Index_PCA<-read.xlsx("../1_Input/colData.xlsx")
rownames(Index_PCA)<-Index_PCA$Sample_ID
#transpose the dataset (required for PCA)
data.pca<-t(PCA_data)
data.pca<-as.data.frame(data.pca)
##merge the file
data.pca_Final<-merge(Index_PCA, data.pca, by=0)
rownames(data.pca_Final)<-data.pca_Final$Row.names
pca.comp<-prcomp(data.pca_Final[,(ncol(Index_PCA)+2):ncol(data.pca_Final)])

pcaCharts=function(x) {
    x.var <- x$sdev ^ 2
    x.pvar <- x.var/sum(x.var)
    par(mfrow=c(2,2))
    plot(x.pvar,xlab="Principal component",
         ylab="Proportion of variance", ylim=c(0,1), type='b')
    plot(cumsum(x.pvar),xlab="Principal component",
         ylab="Cumulative Proportion of variance",
         ylim=c(0,1),
         type='b')
    screeplot(x)
    screeplot(x,type="l")
    par(mfrow=c(1,1))
}
pcaCharts(pca.comp)

png(file=paste0("../2_Output/", COMPARISON,  "/", COMPARISON, "_PCA.Charts.png"))
pcaCharts(pca.comp)
dev.off()
## quartz_off_screen 
##                 2

3-Dimensional PCA

From the previous calculations, it is seens that 3 principal components are necessary, so below is a 3-D PCA to ensure that all groups are characterize to higher-degree of stringency.

##Create a 3D-PCA for Inspection
library(plotly)
##Index
PCA_data<-read.xlsx(paste0("../1_Input/rlog.counts.xlsx"), rowNames = TRUE)
Index_PCA<-read.xlsx("../1_Input/colData.xlsx")
rownames(Index_PCA)<-Index_PCA$Sample_ID
Index_PCA$Group<-factor(Index_PCA$Group)
rownames(Index_PCA)<-Index_PCA$Sample_ID
PCs<-merge(pca.comp$x, Index_PCA, by=0)
rownames(PCs)<-PCs$Row.names
PCs$Group <- as.factor(PCs$Group)
fig <- plot_ly(PCs, x = ~PC1, y = ~PC2, z = ~PC3, color = ~Group, text = ~paste('Sample_ID:', Sample_ID, '<br>Genotype:', Genotype, '<br>Sex:', Sex))
fig <- fig %>% add_markers()
fig <- fig %>% layout(scene = list(xaxis = list(title = 'PC1'),
                     yaxis = list(title = 'PC2'),
                     zaxis = list(title = 'PC3')))
fig

Volcano Plot

To understand the most significant alterations in gene expression, we plotted data according to statistical (-Log10(Pvalue)) and biological (log2(fold-change)) robustness.

# Load packages
library(dplyr)
library(ggplot2)
library(ggrepel)
library(openxlsx)
#DKO Effect in VEH
results = mutate(results, sig_dko.veh=ifelse(results$padj<STATISTIC & abs(results$log2FoldChange)>0.585, paste0("FDR < 0.05 & |FC| > 1.5"), "Not Sig"), minuslogqvalue = -log(padj), log2FC=log2FoldChange) %>% filter(minuslogqvalue!="Inf")
results$sig_dko.veh<-factor(results$sig_dko.veh, levels = c("Not Sig","FDR < 0.05 & |FC| > 1.5"))
max(results$minuslogqvalue, na.rm = TRUE)
## [1] 264.2397
min(results$minuslogqvalue, na.rm = TRUE)
## [1] 0.0001213203
max(results$log2FC, na.rm = TRUE)
## [1] 5.67004
min(results$log2FC, na.rm = TRUE)
## [1] -3.944935
#plot the ggplot
p = ggplot(results, aes(log2FC, minuslogqvalue)) + theme(panel.background = element_rect("white", colour = "black", size=2), panel.grid.major = element_line(colour = "gray50", size=.75), panel.grid.minor = element_line(colour = "gray50", size=0.4)) + 
geom_point(aes(fill=sig_dko.veh, size = minuslogqvalue), colour="grey", shape=21, stroke = 0, alpha = 8/10) + labs(x=expression(Log[2](Fold-Change)), y=expression(-Log[10](FDR))) + xlim(min(results$log2FC, na.rm = TRUE),max(results$log2FC, na.rm = TRUE))+ ylim(-0, max(results$minuslogqvalue, na.rm = TRUE)) +   geom_hline(yintercept = 0, size = 1) + geom_vline(xintercept=0, size=1)+ 
  scale_fill_manual(values=c("grey", "tomato")) +
  scale_size_continuous(range = c(.25, 4))
#add a repelling effect to the text labels.
p+
  geom_text_repel(data=top_n(filter(results, log2FC>0), 10, -padj), aes(label=gene_name)) +
  geom_text_repel(data=top_n(filter(results, log2FC<0), 10, -padj), aes(label=gene_name))

#Print the image
png(file = paste0("../2_Output/", COMPARISON, "/", COMPARISON, "_Volcano.Plot.png"), width = 7.5, height = 5, res = 300, units = "in")
p+
  geom_text_repel(data=top_n(filter(results, log2FC>0), 10, -pvalue), aes(label=gene_name)) +
  geom_text_repel(data=top_n(filter(results, log2FC<0), 10, -pvalue), aes(label=gene_name))
dev.off()
## quartz_off_screen 
##                 2

Heatmap and Clustering of DEGs

To visualize the distribution of DEGs, as well as determine the effect of biological sex on transcription, hierarchical clustering and heatmap visualization were performed at the P < 0.05 statistical level. This analysis reveals that P < 0.05 is sufficient to delineate hierarchical clustering according to genotype, and also higlights sex differences within each.

library(dplyr)
library(pheatmap)
library(ComplexHeatmap)
##
##Index file for annotating samples
rownames(colData_all)<-colData_all$Sample_ID
Index<-subset(colData_all) %>% select(Genotype, Sex) %>% select_if(~ !any(is.na(.)))
Index<-as.data.frame(Index)
# Format heatmap counts Data
results_p05<-filter(results, pvalue<STATISTIC)
rownames(results_p05)<-make.unique(results_p05$gene_name, sep = ".")
normCount_all<-read.xlsx(paste0("../1_Input/Normalized.Counts.xlsx"), rowNames = F)
## Remove outliers
vector<-rownames(Index)
normCount_all<-select(normCount_all, `results$gene_name`, any_of(vector))
## Create heatmap matrix
hm_data<-subset(normCount_all, `results$gene_name` %in% results_p05$gene_name) 
rownames(hm_data)<-make.unique(hm_data$`results$gene_name`, sep = ".")
hm_data<- hm_data %>% select(-`results$gene_name`) %>% data.matrix()
#Create heatmaps on summary data
paletteLength <- 100
myColor <- colorRampPalette(c("dodgerblue4", "white", "gold2"))(paletteLength)
pheatmap::pheatmap(hm_data, scale="row",
         cluster_cols = TRUE,
         cluster_rows = TRUE,
         #breaks = myBreaks,
        # cutree_cols = 2,
         cutree_rows = 2,
         angle_col = 45,
         fontsize_col = 8,
         color = myColor,
         show_rownames = FALSE,
         border_color = NA,
         annotation_colors = GROUP_colors,
         annotation_col = Index,
         filename=paste0("../2_Output/", COMPARISON,  "/", COMPARISON,"_Heatmap_Normcount_P05.pdf"))
vst<-varianceStabilizingTransformation(data.matrix(countData.raw))
normhm<-vst[row.names(resdf[which(resdf$pvalue<STATISTIC),]),]
normhm<-scale(t(normhm))
normhm<-t(normhm)
colData_exp<-subset(colData_all, Sex %in% SEX, Genotype %in% Genotype)
normhm<-as.data.frame(normhm) %>% select(any_of(rownames(colData_exp)))
pheatmap::pheatmap(normhm, #Variance stabilized transformation
         cluster_cols=T,
         clustering_method = "ward.D2",
         border_color=NA,
         cutree_cols = 2,
         cutree_rows = 4,
         cluster_rows=T,
         scale = 'row',
         show_colnames = F,
         show_rownames = F,
         annotation_colors = GROUP_colors,
         color = myColor,
         annotation_col = colData_exp,
         filename=paste0("../2_Output/", COMPARISON,  "/", COMPARISON,"_VST.Heatmap.P05.pdf"))

## Create a heatmap on only the samples used in the comparison
normhm_group<-dplyr::select(as.data.frame(normhm), any_of(vector)) %>% data.matrix() 
normhm_group<-t(scale(t(normhm_group)))

heat<-pheatmap::pheatmap(normhm_group, #Variance stabilized transformation
         cluster_cols=T,
         clustering_method = "ward.D2",
         border_color=NA,
         cluster_rows=T,
         scale = 'row',
         show_colnames = T,
         show_rownames = F,
         annotation_colors = GROUP_colors,
         color = myColor,
         annotation_col = Index)

Candidate Gene Expression

To examine individual genes, I created a script that can search for genes and plot with statistics (student’s t-test). in the Parameters section, enter a gene name in the GENES vector and a graph will be produced in the “../2_Output/Candidates” folder.

library(ggplot2)
library(gridExtra)
library(ggpubr)
library(dplyr)
library(gtools)
library(openxlsx)
ifelse(!dir.exists(file.path(paste0("../2_Output/Candidates"))), dir.create(file.path(paste0("../2_Output/Candidates"))), FALSE)
## [1] FALSE
#Import Index file
colData<-openxlsx::read.xlsx("../1_Input/colData.xlsx",  startRow = 1)
colData$Sample_ID<-as.character(colData$Sample_ID)
# colData<-colData %>% filter(Sex=="F") # Select sex (if applicable)
##Import normalized counts
Counts<-read.xlsx(paste0("../1_Input/Normalized.Counts.xlsx"), rowNames = F)
#Remove outliers
vector<-colData$Sample_ID
Counts<-dplyr::select(Counts, any_of(vector), `results$gene_name`) %>% rename(gene_name=`results$gene_name`)

#Filter results by the gene vector
DEGs<-subset(Counts, gene_name %in% GENES)
rownames(DEGs)<-make.unique(as.character(DEGs$gene_name, sep = "."))
tDEGs<-as.data.frame(t(DEGs))
## convert all genes to numeric (from factors)
asNumeric=function(x){as.numeric(as.character(x))}
factorsNumeric=function(d){modifyList(d, lapply(d[, sapply(d, is.character)], asNumeric))}
##
tDEGs<-factorsNumeric(tDEGs)
tDEGs$Sample_ID<-rownames(tDEGs)
colData.ex<-dplyr::inner_join(tDEGs, colData)
colData.ex$Group<-factor(colData.ex$Group, levels = c("F_WT","M_WT","F_RBM20.KO", "M_RBM20.KO"))
colData.ex<-dplyr::group_by_(colData.ex, "Group") #define groups for statistics
write.xlsx(colData.ex, paste0("../2_Output/Candidates/Candidate_genes.xlsx"), overwrite = T)
## For loop creating a graph for each gene
plotlist = list()
p<-1
for (i in GENES){
g_plot<-ggboxplot(colData.ex, x = "Group", 
          y = i, 
          fill = "Group",
          add = "jitter"
          ) + 
  scale_fill_manual(values = ann_colorInvestVec) +
  stat_compare_means(aes(group = Group),
                    comparisons = my_comparisons_all,
                    label = "p.signif",
                    bracket.nudge.y = 5
                    ) +
  theme(axis.text.x=element_text(size=rel(0.75), angle = 45, hjust = 1), axis.text.y=element_text(size=rel(0.75)), axis.title.x = element_blank(), axis.title.y = element_text(face = "bold"), legend.position="none") + # resize labels, remove legend
  scale_y_continuous(expand = expansion(mult = c(0, 0.2))) + # expand = expansion(mult = c(0, 0.1)) ### Y scale (to see the statistics)
  geom_text_repel(aes(label=Sample_ID), color = "grey", size = 2) # Add Sample_ID

g_plot

pdf(file=paste0("../2_Output/Candidates/", i, "_Expression_Male.pdf"), width = 6, height = 6)
print(g_plot)
dev.off()
plotlist[[i]] = g_plot
}
t<-marrangeGrob(grobs = plotlist, legend, nrow=2, ncol=2)
ggsave(paste0("../2_Output/Candidates/DEGs.pdf"), t, width = 6, height = 7)
t

Promoter-based Motif Enrichment in HOMER

Motif Pre-processing

In order to determine the relative enrichment of response elements in the proximal promoters of differentially-expressed genes, the first step is to define the lists of up-regulated and down-regulated genes. Furthermore, because motif-enrichment requires the use of system commands (i.e. HOMER), R variables should be exported as system variables to preserve the analysis pipeline.

homer_tS<-Sys.time()
library(dplyr)
GeneList.up<-dplyr::filter(results_p05, log2FoldChange>.454) %>% .$gene_name
GeneList.down<-dplyr::filter(results_p05, log2FoldChange< -.454) %>% .$gene_name
#Create the gene list for motif Enrichment (UP)
fileConn<-file(paste0("../2_Output/", COMPARISON, "/", "DEGS_UP.txt")) 
writeLines(GeneList.up, fileConn)
close(fileConn)
#Create the gene list for motif Enrichment (DOWN)
fileConn<-file(paste0("../2_Output/", COMPARISON, "/", "DEGS_DOWN.txt")) 
writeLines(GeneList.down, fileConn)
close(fileConn)

Run HOMER from bash terminal

Now HOMER can be run using a bash script within this session. Note that proper setup of HOMER is needed to execute this function, including designation of PATH=$PATH:/Users/chucknorris/homer/bin/ within the ~/.bash_profile file; sourcing this file (see below) connects the HOMER software with the rmarkdown session.

Pathway Enrichment

library(dplyr)
library(pathview)
library(biomaRt)
library(openxlsx)
futile.logger::flog.threshold(futile.logger::ERROR, name = "VennDiagramLogger") # Stop the Venn Diagram from making a log file...
## NULL
F_RBM.v.WT<-read.xlsx("../2_Output/F_RBM20.KO.v.WT/F_RBM20.KO.v.WT_DESeq2.xlsx", sheet = "Q_0.05")
M_RBM.v.WT<-read.xlsx("../2_Output/M_RBM20.KO.v.WT/M_RBM20.KO.v.WT_DESeq2.xlsx", sheet = "Q_0.05")
# Female Only DEGs
F_RBM.v.WT_UP<-filter(F_RBM.v.WT, log2FoldChange>0.585)
F_RBM.v.WT_DOWN<-filter(F_RBM.v.WT, log2FoldChange< -0.585)
F_RBM.v.WT_ONLY<-anti_join(F_RBM.v.WT, M_RBM.v.WT, by = "gene_name")
F_RBM.v.WT_ONLY.UP<-F_RBM.v.WT_ONLY %>% filter(log2FoldChange>0.585)
F_RBM.v.WT_ONLY.DOWN<-F_RBM.v.WT_ONLY %>% filter(log2FoldChange< -0.585)
# Male Only DEGs
M_RBM.v.WT_UP<-filter(M_RBM.v.WT, log2FoldChange>0.585)
M_RBM.v.WT_DOWN<-filter(M_RBM.v.WT, log2FoldChange< -0.585)
M_RBM.v.WT_ONLY<-anti_join(M_RBM.v.WT, F_RBM.v.WT, by = "gene_name")
M_RBM.v.WT_ONLY.UP<-M_RBM.v.WT_ONLY %>% filter(log2FoldChange>0.585)
M_RBM.v.WT_ONLY.DOWN<-M_RBM.v.WT_ONLY %>% filter(log2FoldChange< -0.585)

# Overlapping DEGs
Conserved_DEGs<-inner_join(F_RBM.v.WT, M_RBM.v.WT, by = "ensembl_gene_id") %>% dplyr::rename(GeneName = gene_name.x) %>% rename_all(~stringr::str_replace_all(.,c(".y"="_M_RBM.v.WT", ".x"="_F_RBM.v.WT")))
rownames(Conserved_DEGs)<-make.unique(Conserved_DEGs$GeneName, sep = ".")
Conserved_Both.UP<-Conserved_DEGs %>% filter(log2FoldChange_F_RBM.v.WT > 0.585, log2FoldChange_M_RBM.v.WT > 0.585)
Conserved_Both.DOWN<-Conserved_DEGs %>% filter(log2FoldChange_F_RBM.v.WT < -0.585, log2FoldChange_M_RBM.v.WT < -0.585)
Conserved_Inverse<-Conserved_DEGs %>% filter((log2FoldChange_F_RBM.v.WT>0.585 & log2FoldChange_M_RBM.v.WT< -0.585) | (log2FoldChange_F_RBM.v.WT< -0.585 & log2FoldChange_M_RBM.v.WT>0.585))
write.xlsx(Conserved_DEGs, "../2_Output/conserved.DEGs.xlsx", overwrite = TRUE)

library(openxlsx)
wb_DESeq<-createWorkbook()
#Unfiltered
  addWorksheet(wb_DESeq, "F_RBM.v.WT_ONLY")
  writeData(wb_DESeq, "F_RBM.v.WT_ONLY", F_RBM.v.WT_ONLY, startCol = 1)
  addWorksheet(wb_DESeq, "M_RBM.v.WT_ONLY")
  writeData(wb_DESeq, "M_RBM.v.WT_ONLY", M_RBM.v.WT_ONLY, startCol = 1)
  addWorksheet(wb_DESeq, "Conserved_DEGs")
  writeData(wb_DESeq, "Conserved_DEGs", Conserved_DEGs, startCol = 1)
saveWorkbook(wb_DESeq, file = paste0("../2_Output/Sex.Comparison_RBM.v.WT.xlsx"), overwrite = TRUE)
########### VENN DIAGRAM
x<-list(M_RBM.v.WT = M_RBM.v.WT$gene_name, F_RBM.v.WT = F_RBM.v.WT$gene_name)
library(VennDiagram)
venn.diagram(x,fill = c("red", "grey"), alpha = c(0.75, 0.75), lty = 'blank', filename = "../2_Output/Overlap.pdf", na = "remove")
## [1] 1
##Enrichr
library(enrichR)
Test<-listEnrichrDbs()
dbs <- c("WikiPathway_2021_Human")
enriched_F_RBM.v.WT<-enrichr(F_RBM.v.WT$gene_name, dbs)
## Uploading data to Enrichr... Done.
##   Querying WikiPathway_2021_Human... Done.
## Parsing results... Done.
enrich.F_RBM.v.WT<-enriched_F_RBM.v.WT[[dbs]]
enriched_F_RBM.v.WT_UP <- enrichr(F_RBM.v.WT_UP$gene_name, dbs)
## Uploading data to Enrichr... Done.
##   Querying WikiPathway_2021_Human... Done.
## Parsing results... Done.
enrich.F_RBM.v.WT_UP<-enriched_F_RBM.v.WT_UP[[dbs]]
enriched_F_RBM.v.WT_DOWN <- enrichr(F_RBM.v.WT_DOWN$gene_name, dbs)
## Uploading data to Enrichr... Done.
##   Querying WikiPathway_2021_Human... Done.
## Parsing results... Done.
enrich.F_RBM.v.WT_DOWN<-enriched_F_RBM.v.WT_DOWN[[dbs]]
# F_RBM.v.WT Only
enriched_F_RBM.v.WT_ONLY.UP <- enrichr(F_RBM.v.WT_ONLY.UP$gene_name, dbs)
## Uploading data to Enrichr... Done.
##   Querying WikiPathway_2021_Human... Done.
## Parsing results... Done.
enrich.F_RBM.v.WT_ONLY.UP<-enriched_F_RBM.v.WT_ONLY.UP[[dbs]]
enriched_F_RBM.v.WT_ONLY.DOWN <- enrichr(F_RBM.v.WT_ONLY.DOWN$gene_name, dbs)
## Uploading data to Enrichr... Done.
##   Querying WikiPathway_2021_Human... Done.
## Parsing results... Done.
enrich.F_RBM.v.WT_ONLY.DOWN<-enriched_F_RBM.v.WT_ONLY.DOWN[[dbs]]
#M_RBM.v.WT
enriched_M_RBM.v.WT <- enrichr(M_RBM.v.WT$gene_name, dbs)
## Uploading data to Enrichr... Done.
##   Querying WikiPathway_2021_Human... Done.
## Parsing results... Done.
enrich.M_RBM.v.WT<-enriched_M_RBM.v.WT[[dbs]]
enriched_M_RBM.v.WT_UP <- enrichr(M_RBM.v.WT_UP$gene_name, dbs)
## Uploading data to Enrichr... Done.
##   Querying WikiPathway_2021_Human... Done.
## Parsing results... Done.
enrich.M_RBM.v.WT_UP<-enriched_M_RBM.v.WT_UP[[dbs]]
enriched_M_RBM.v.WT_DOWN <- enrichr(M_RBM.v.WT_DOWN$gene_name, dbs)
## Uploading data to Enrichr... Done.
##   Querying WikiPathway_2021_Human... Done.
## Parsing results... Done.
enrich.M_RBM.v.WT_DOWN<-enriched_M_RBM.v.WT_DOWN[[dbs]]
#M_RBM.v.WT ONLY
enriched_M_RBM.v.WT_ONLY.UP <- enrichr(M_RBM.v.WT_ONLY.UP$gene_name, dbs)
## Uploading data to Enrichr... Done.
##   Querying WikiPathway_2021_Human... Done.
## Parsing results... Done.
enrich.M_RBM.v.WT_ONLY.UP<-enriched_M_RBM.v.WT_ONLY.UP[[dbs]]
enriched_M_RBM.v.WT_ONLY.DOWN <- enrichr(M_RBM.v.WT_ONLY.DOWN$gene_name, dbs)
## Uploading data to Enrichr... Done.
##   Querying WikiPathway_2021_Human... Done.
## Parsing results... Done.
enrich.M_RBM.v.WT_ONLY.DOWN<-enriched_M_RBM.v.WT_ONLY.DOWN[[dbs]]
#Overlap
enriched_Conserved_Both.UP <- enrichr(Conserved_Both.UP$GeneName, dbs)
## Uploading data to Enrichr... Done.
##   Querying WikiPathway_2021_Human... Done.
## Parsing results... Done.
enrich.Conserved_Both.UP<-enriched_Conserved_Both.UP[[dbs]]
enriched_Conserved_Both.DOWN <- enrichr(Conserved_Both.DOWN$GeneName, dbs)
## Uploading data to Enrichr... Done.
##   Querying WikiPathway_2021_Human... Done.
## Parsing results... Done.
enrich.Conserved_Both.DOWN<-enriched_Conserved_Both.DOWN[[dbs]]

library(openxlsx)
wb_DESeq<-createWorkbook()
#Unfiltered
  addWorksheet(wb_DESeq, "F_RBM.v.WT_UP")
  writeData(wb_DESeq, "F_RBM.v.WT_UP", enrich.F_RBM.v.WT_UP, startCol = 1)
  addWorksheet(wb_DESeq, "F_RBM.v.WT_DOWN")
  writeData(wb_DESeq, "F_RBM.v.WT_DOWN", enrich.F_RBM.v.WT_DOWN, startCol = 1)
  addWorksheet(wb_DESeq, "F_RBM.v.WT.ONLY_UP")
  writeData(wb_DESeq, "F_RBM.v.WT.ONLY_UP", enrich.F_RBM.v.WT_ONLY.UP, startCol = 1)
  addWorksheet(wb_DESeq, "F_RBM.v.WT.ONLY_DOWN")
  writeData(wb_DESeq, "F_RBM.v.WT.ONLY_DOWN", enrich.F_RBM.v.WT_ONLY.DOWN, startCol = 1)  
  addWorksheet(wb_DESeq, "M_RBM.v.WT_UP")
  writeData(wb_DESeq, "M_RBM.v.WT_UP", enrich.M_RBM.v.WT_UP, startCol = 1)
  addWorksheet(wb_DESeq, "M_RBM.v.WT_DOWN")
  writeData(wb_DESeq, "M_RBM.v.WT_DOWN", enrich.M_RBM.v.WT_DOWN, startCol = 1)
  addWorksheet(wb_DESeq, "M_RBM.v.WT.ONLY_UP")
  writeData(wb_DESeq, "M_RBM.v.WT.ONLY_UP", enrich.M_RBM.v.WT_ONLY.UP, startCol = 1)
  addWorksheet(wb_DESeq, "M_RBM.v.WT.ONLY_DOWN")
  writeData(wb_DESeq, "M_RBM.v.WT.ONLY_DOWN", enrich.M_RBM.v.WT_ONLY.DOWN, startCol = 1)  
saveWorkbook(wb_DESeq, file = paste0("../2_Output/Pathway.Enrichment_Enrichr_", dbs,".xlsx"), overwrite = TRUE)

Differential Exon Usage

Supplemental Table: R Session Information

All packages and settings seen below.

# unloading system variables after session
homer_tE<-Sys.time()
homer_t<-homer_tE - homer_tS
homer_t
## Time difference of 32.22408 secs
end_t<-Sys.time()
Total_time<-end_t - start_t
Total_time
## Time difference of 3.007866 mins
Sys.unsetenv("motifLoc_UP")
Sys.unsetenv("motifLoc_DOWN")
Sys.unsetenv("motifLoc_WD")
# Write 
options(kableExtra.latex.load_packages = FALSE)
library(kableExtra)
sinfo<-devtools::session_info()
sinfo$platform
##  setting  value
##  version  R version 4.1.2 (2021-11-01)
##  os       macOS Big Sur 10.16
##  system   x86_64, darwin17.0
##  ui       X11
##  language (EN)
##  collate  en_US.UTF-8
##  ctype    en_US.UTF-8
##  tz       Europe/Berlin
##  date     2022-05-01
##  pandoc   2.14.0.3 @ /Applications/RStudio.app/Contents/MacOS/pandoc/ (via rmarkdown)
sinfo$packages %>% kable(
                         align="c",
                         longtable=T,
                         booktabs=T,
                         caption="Packages and Required Dependencies") %>%
    kable_styling(latex_options=c("striped", "repeat_header", "condensed"))
Packages and Required Dependencies
package ondiskversion loadedversion path loadedpath attached is_base date source md5ok library
abind abind 1.4.5 1.4-5 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/abind /Library/Frameworks/R.framework/Versions/4.1/Resources/library/abind FALSE FALSE 2016-07-21 CRAN (R 4.1.0) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
annotate annotate 1.72.0 1.72.0 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/annotate /Library/Frameworks/R.framework/Versions/4.1/Resources/library/annotate FALSE FALSE 2021-10-26 Bioconductor /Library/Frameworks/R.framework/Versions/4.1/Resources/library
AnnotationDbi AnnotationDbi 1.56.2 1.56.2 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/AnnotationDbi /Library/Frameworks/R.framework/Versions/4.1/Resources/library/AnnotationDbi TRUE FALSE 2021-11-09 Bioconductor /Library/Frameworks/R.framework/Versions/4.1/Resources/library
assertthat assertthat 0.2.1 0.2.1 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/assertthat /Library/Frameworks/R.framework/Versions/4.1/Resources/library/assertthat FALSE FALSE 2019-03-21 CRAN (R 4.1.0) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
backports backports 1.4.1 1.4.1 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/backports /Library/Frameworks/R.framework/Versions/4.1/Resources/library/backports FALSE FALSE 2021-12-13 CRAN (R 4.1.0) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
base64enc base64enc 0.1.3 0.1-3 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/base64enc /Library/Frameworks/R.framework/Versions/4.1/Resources/library/base64enc FALSE FALSE 2015-07-28 CRAN (R 4.1.0) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
Biobase Biobase 2.54.0 2.54.0 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/Biobase /Library/Frameworks/R.framework/Versions/4.1/Resources/library/Biobase TRUE FALSE 2021-10-26 Bioconductor /Library/Frameworks/R.framework/Versions/4.1/Resources/library
BiocFileCache BiocFileCache 2.2.0 2.2.0 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/BiocFileCache /Library/Frameworks/R.framework/Versions/4.1/Resources/library/BiocFileCache FALSE FALSE 2021-10-26 Bioconductor /Library/Frameworks/R.framework/Versions/4.1/Resources/library
BiocGenerics BiocGenerics 0.40.0 0.40.0 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/BiocGenerics /Library/Frameworks/R.framework/Versions/4.1/Resources/library/BiocGenerics TRUE FALSE 2021-10-26 Bioconductor /Library/Frameworks/R.framework/Versions/4.1/Resources/library
BiocIO BiocIO 1.4.0 1.4.0 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/BiocIO /Library/Frameworks/R.framework/Versions/4.1/Resources/library/BiocIO FALSE FALSE 2021-10-26 Bioconductor /Library/Frameworks/R.framework/Versions/4.1/Resources/library
BiocParallel BiocParallel 1.28.3 1.28.3 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/BiocParallel /Library/Frameworks/R.framework/Versions/4.1/Resources/library/BiocParallel FALSE FALSE 2021-12-09 Bioconductor /Library/Frameworks/R.framework/Versions/4.1/Resources/library
biomaRt biomaRt 2.50.1 2.50.1 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/biomaRt /Library/Frameworks/R.framework/Versions/4.1/Resources/library/biomaRt TRUE FALSE 2021-11-21 Bioconductor /Library/Frameworks/R.framework/Versions/4.1/Resources/library
Biostrings Biostrings 2.62.0 2.62.0 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/Biostrings /Library/Frameworks/R.framework/Versions/4.1/Resources/library/Biostrings FALSE FALSE 2021-10-26 Bioconductor /Library/Frameworks/R.framework/Versions/4.1/Resources/library
bit bit 4.0.4 4.0.4 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/bit /Library/Frameworks/R.framework/Versions/4.1/Resources/library/bit FALSE FALSE 2020-08-04 CRAN (R 4.1.0) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
bit64 bit64 4.0.5 4.0.5 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/bit64 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/bit64 FALSE FALSE 2020-08-30 CRAN (R 4.1.0) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
bitops bitops 1.0.7 1.0-7 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/bitops /Library/Frameworks/R.framework/Versions/4.1/Resources/library/bitops FALSE FALSE 2021-04-24 CRAN (R 4.1.0) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
blob blob 1.2.2 1.2.2 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/blob /Library/Frameworks/R.framework/Versions/4.1/Resources/library/blob FALSE FALSE 2021-07-23 CRAN (R 4.1.0) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
broom broom 0.7.11 0.7.11 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/broom /Library/Frameworks/R.framework/Versions/4.1/Resources/library/broom FALSE FALSE 2022-01-03 CRAN (R 4.1.2) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
bslib bslib 0.3.1 0.3.1 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/bslib /Library/Frameworks/R.framework/Versions/4.1/Resources/library/bslib FALSE FALSE 2021-10-06 CRAN (R 4.1.0) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
cachem cachem 1.0.6 1.0.6 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/cachem /Library/Frameworks/R.framework/Versions/4.1/Resources/library/cachem FALSE FALSE 2021-08-19 CRAN (R 4.1.0) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
calibrate calibrate 1.7.7 1.7.7 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/calibrate /Library/Frameworks/R.framework/Versions/4.1/Resources/library/calibrate TRUE FALSE 2020-06-19 CRAN (R 4.1.0) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
callr callr 3.7.0 3.7.0 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/callr /Library/Frameworks/R.framework/Versions/4.1/Resources/library/callr FALSE FALSE 2021-04-20 CRAN (R 4.1.0) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
car car 3.0.12 3.0-12 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/car /Library/Frameworks/R.framework/Versions/4.1/Resources/library/car FALSE FALSE 2021-11-06 CRAN (R 4.1.0) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
carData carData 3.0.5 3.0-5 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/carData /Library/Frameworks/R.framework/Versions/4.1/Resources/library/carData FALSE FALSE 2022-01-06 CRAN (R 4.1.2) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
checkmate checkmate 2.0.0 2.0.0 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/checkmate /Library/Frameworks/R.framework/Versions/4.1/Resources/library/checkmate FALSE FALSE 2020-02-06 CRAN (R 4.1.0) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
circlize circlize 0.4.13 0.4.13 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/circlize /Library/Frameworks/R.framework/Versions/4.1/Resources/library/circlize FALSE FALSE 2021-06-09 CRAN (R 4.1.0) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
cli cli 3.1.0 3.1.0 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/cli /Library/Frameworks/R.framework/Versions/4.1/Resources/library/cli FALSE FALSE 2021-10-27 CRAN (R 4.1.0) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
clue clue 0.3.60 0.3-60 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/clue /Library/Frameworks/R.framework/Versions/4.1/Resources/library/clue FALSE FALSE 2021-10-11 CRAN (R 4.1.0) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
cluster cluster 2.1.2 2.1.2 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/cluster /Library/Frameworks/R.framework/Versions/4.1/Resources/library/cluster FALSE FALSE 2021-04-17 CRAN (R 4.1.2) /Library/Frameworks/R.framework/Versions/4.1/Resources/library
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